Singlet gating in mass cytometry

Gating mass cytometry data by deep learning

Motivation Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types...

flowUtils: Gating-ML Support in Flow Cytometry

Gating in flow cytometry is a highly important process for selecting populations of interests by defining the characteristics of particles for further data acquisition or analysis. GatingML represents a specification on how to form unambiguous XML-based gate definitions. Such a description of gates facilitates the interchange and validation of data and analysis among different software packages...

flowMeans: Non-parametric Flow Cytometry Data Gating

Transfer Learning for Auto-gating of Flow Cytometry Data

Flow cytometry is a technique for rapidly quantifying physical and chemical properties of large numbers of cells. In clinical applications, flow cytometry data must be manually “gated” to identify cell populations of interest. While several researchers have investigated statistical methods for automating this process, most of them falls under the framework of unsupervised learning and mixture m...

Transfer Learning for Automatic Gating of Flow Cytometry Data

Flow cytometry is a technique for rapidly quantifying physical and chemical properties of large numbers of cells. In clinical applications, flow cytometry data must be manually “gated” to identify cell populations of interest. Because multiple, iterative gates are often required to identify and characterize these populations, several researchers have investigated statistical methods for automat...